目录
强大的性能,无限的扩展能力
收集、组织和处理海量高速数据。当您将任何数据视为时间序列数据时,它会更有价值。借助 InfluxDB,排名第一的时间序列平台,旨在与 Telegraf 一起扩展。
查看入门方法
输入和输出集成概述
HTTP 插件允许从指定的 HTTP 端点收集指标,处理各种数据格式和身份验证方法。
Telegraf PostgreSQL 插件允许您有效地将指标写入 PostgreSQL 数据库,同时自动管理数据库架构。
集成详情
HTTP
HTTP 插件从一个或多个 HTTP(S) 端点收集指标,这些端点应具有以受支持的输入数据格式之一格式化的指标。它还支持来自密钥存储的密钥,用于各种身份验证选项,并包括全局支持的配置设置。
PostgreSQL
PostgreSQL 插件使用户能够将指标写入 PostgreSQL 数据库或兼容数据库,为模式管理提供强大的支持,通过自动更新缺失的列。该插件旨在促进与监控解决方案的集成,允许用户有效地存储和管理时间序列数据。它为连接设置、并发和错误处理提供可配置的选项,并支持高级功能,例如用于标签和字段的 JSONB 存储、外键标记、模板化模式修改以及通过 pguint 扩展支持无符号整数数据类型。
配置
HTTP
[[inputs.http]]
## One or more URLs from which to read formatted metrics.
urls = [
"http://localhost/metrics",
"http+unix:///run/user/420/podman/podman.sock:/d/v4.0.0/libpod/pods/json"
]
## HTTP method
# method = "GET"
## Optional HTTP headers
# headers = {"X-Special-Header" = "Special-Value"}
## HTTP entity-body to send with POST/PUT requests.
# body = ""
## HTTP Content-Encoding for write request body, can be set to "gzip" to
## compress body or "identity" to apply no encoding.
# content_encoding = "identity"
## Optional Bearer token settings to use for the API calls.
## Use either the token itself or the token file if you need a token.
# token = "eyJhbGc...Qssw5c"
# token_file = "/path/to/file"
## Optional HTTP Basic Auth Credentials
# username = "username"
# password = "pa$$word"
## OAuth2 Client Credentials. The options 'client_id', 'client_secret', and 'token_url' are required to use OAuth2.
# client_id = "clientid"
# client_secret = "secret"
# token_url = "https://indentityprovider/oauth2/v1/token"
# scopes = ["urn:opc:idm:__myscopes__"]
## HTTP Proxy support
# use_system_proxy = false
# http_proxy_url = ""
## Optional TLS Config
## Set to true/false to enforce TLS being enabled/disabled. If not set,
## enable TLS only if any of the other options are specified.
# tls_enable =
## Trusted root certificates for server
# tls_ca = "/path/to/cafile"
## Used for TLS client certificate authentication
# tls_cert = "/path/to/certfile"
## Used for TLS client certificate authentication
# tls_key = "/path/to/keyfile"
## Password for the key file if it is encrypted
# tls_key_pwd = ""
## Send the specified TLS server name via SNI
# tls_server_name = "kubernetes.example.com"
## Minimal TLS version to accept by the client
# tls_min_version = "TLS12"
## List of ciphers to accept, by default all secure ciphers will be accepted
## See https://pkg.go.dev/crypto/tls#pkg-constants for supported values.
## Use "all", "secure" and "insecure" to add all support ciphers, secure
## suites or insecure suites respectively.
# tls_cipher_suites = ["secure"]
## Renegotiation method, "never", "once" or "freely"
# tls_renegotiation_method = "never"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
## Optional Cookie authentication
# cookie_auth_url = "https://localhost/authMe"
# cookie_auth_method = "POST"
# cookie_auth_username = "username"
# cookie_auth_password = "pa$$word"
# cookie_auth_headers = { Content-Type = "application/json", X-MY-HEADER = "hello" }
# cookie_auth_body = '{"username": "user", "password": "pa$$word", "authenticate": "me"}'
## cookie_auth_renewal not set or set to "0" will auth once and never renew the cookie
# cookie_auth_renewal = "5m"
## Amount of time allowed to complete the HTTP request
# timeout = "5s"
## List of success status codes
# success_status_codes = [200]
## Data format to consume.
## Each data format has its own unique set of configuration options, read
## more about them here:
## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md
# data_format = "influx"
PostgreSQL
# Publishes metrics to a postgresql database
[[outputs.postgresql]]
## Specify connection address via the standard libpq connection string:
## host=... user=... password=... sslmode=... dbname=...
## Or a URL:
## postgres://[user[:password]]@localhost[/dbname]?sslmode=[disable|verify-ca|verify-full]
## See https://postgresql.ac.cn/docs/current/libpq-connect.html#LIBPQ-CONNSTRING
##
## All connection parameters are optional. Environment vars are also supported.
## e.g. PGPASSWORD, PGHOST, PGUSER, PGDATABASE
## All supported vars can be found here:
## https://postgresql.ac.cn/docs/current/libpq-envars.html
##
## Non-standard parameters:
## pool_max_conns (default: 1) - Maximum size of connection pool for parallel (per-batch per-table) inserts.
## pool_min_conns (default: 0) - Minimum size of connection pool.
## pool_max_conn_lifetime (default: 0s) - Maximum age of a connection before closing.
## pool_max_conn_idle_time (default: 0s) - Maximum idle time of a connection before closing.
## pool_health_check_period (default: 0s) - Duration between health checks on idle connections.
# connection = ""
## Postgres schema to use.
# schema = "public"
## Store tags as foreign keys in the metrics table. Default is false.
# tags_as_foreign_keys = false
## Suffix to append to table name (measurement name) for the foreign tag table.
# tag_table_suffix = "_tag"
## Deny inserting metrics if the foreign tag can't be inserted.
# foreign_tag_constraint = false
## Store all tags as a JSONB object in a single 'tags' column.
# tags_as_jsonb = false
## Store all fields as a JSONB object in a single 'fields' column.
# fields_as_jsonb = false
## Name of the timestamp column
## NOTE: Some tools (e.g. Grafana) require the default name so be careful!
# timestamp_column_name = "time"
## Type of the timestamp column
## Currently, "timestamp without time zone" and "timestamp with time zone"
## are supported
# timestamp_column_type = "timestamp without time zone"
## Templated statements to execute when creating a new table.
# create_templates = [
# '''CREATE TABLE {{ .table }} ({{ .columns }})''',
# ]
## Templated statements to execute when adding columns to a table.
## Set to an empty list to disable. Points containing tags for which there is no column will be skipped. Points
## containing fields for which there is no column will have the field omitted.
# add_column_templates = [
# '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
# ]
## Templated statements to execute when creating a new tag table.
# tag_table_create_templates = [
# '''CREATE TABLE {{ .table }} ({{ .columns }}, PRIMARY KEY (tag_id))''',
# ]
## Templated statements to execute when adding columns to a tag table.
## Set to an empty list to disable. Points containing tags for which there is no column will be skipped.
# tag_table_add_column_templates = [
# '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
# ]
## The postgres data type to use for storing unsigned 64-bit integer values (Postgres does not have a native
## unsigned 64-bit integer type).
## The value can be one of:
## numeric - Uses the PostgreSQL "numeric" data type.
## uint8 - Requires pguint extension (https://github.com/petere/pguint)
# uint64_type = "numeric"
## When using pool_max_conns>1, and a temporary error occurs, the query is retried with an incremental backoff. This
## controls the maximum backoff duration.
# retry_max_backoff = "15s"
## Approximate number of tag IDs to store in in-memory cache (when using tags_as_foreign_keys).
## This is an optimization to skip inserting known tag IDs.
## Each entry consumes approximately 34 bytes of memory.
# tag_cache_size = 100000
## Enable & set the log level for the Postgres driver.
# log_level = "warn" # trace, debug, info, warn, error, none
输入和输出集成示例
HTTP
- 从本地主机收集指标: 该插件可以从 HTTP 端点(如
http://localhost/metrics
)获取指标,从而实现简单的本地监控。 - 使用 Unix 域套接字: 您可以通过使用 http+unix 方案指定从 Unix 域套接字上的服务收集指标,例如
http+unix:///path/to/service.sock:/api/endpoint
。
PostgreSQL
-
使用复杂查询进行实时分析:利用 PostgreSQL 插件将来自各种来源的指标存储在 PostgreSQL 数据库中,从而可以使用复杂查询进行实时分析。这种设置可以帮助数据科学家和分析师发现模式和趋势,因为他们可以跨多个表操作关系数据,同时利用 PostgreSQL 强大的查询优化功能。具体来说,用户可以使用跨不同指标表的 JOIN 操作创建复杂的报告,从而揭示通常在嵌入式系统中仍然隐藏的见解。
-
与 TimescaleDB 集成以进行时间序列数据处理:在 TimescaleDB 实例中使用 PostgreSQL 插件,以高效地处理和分析时间序列数据。通过实施超表,用户可以在时间维度上实现更高的性能和主题分区。这种集成允许用户对大量时间序列数据运行分析查询,同时保留 PostgreSQL SQL 查询的全部功能,从而确保指标分析的可靠性和效率。
-
数据版本控制和历史分析:实施使用 PostgreSQL 插件的策略,以维护指标的不同版本。用户可以设置不可变的数据表结构,其中保留旧版本的表,从而轻松进行历史分析。这种方法不仅提供了对数据演变的洞察力,还有助于遵守数据保留策略,确保数据集的历史完整性保持不变。
-
用于不断发展的指标的动态模式管理:使用插件的模板功能创建动态变化的模式,以响应指标变化。此用例允许组织在指标发展时调整其数据结构,添加必要的字段并确保遵守数据完整性策略。通过利用模板化的 SQL 命令,用户无需手动干预即可扩展其数据库,从而促进敏捷的数据管理实践。
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强大的性能,无限的扩展能力
收集、组织和处理海量高速数据。当您将任何数据视为时间序列数据时,它会更有价值。借助 InfluxDB,排名第一的时间序列平台,旨在与 Telegraf 一起扩展。
查看入门方法